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Proceedings Paper

Association between bilateral asymmetry of kinetic features computed from the DCE-MRI images and breast cancer
Author(s): Qian Yang; Lihua Li; Juan Zhang; Chengjie Zhang; Bin Zheng
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Paper Abstract

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) of breast yields high sensitivity but relatively lower specificity. To improve diagnostic accuracy of DCE-MRI, we investigated the association between bilateral asymmetry of kinetic features computed from the left and right breasts and breast cancer detection with the hypothesis that due to the growth of angiogenesis associated with malignant lesions, the average dynamic contrast enhancement computed from the breasts depicting malignant lesions should be higher than negative or benign breasts. To test this hypothesis, we assembled a database involving 130 DCE-MRI examinations including 81 malignant and 49 benign cases. We developed a computerized scheme that automatically segments breast areas depicted on MR images and computes kinetic features related to the bilateral asymmetry of contrast enhancement ratio between two breasts. An artificial neural network (ANN) was then used to classify between malignant and benign cases. To identify the optimal approach to compute the bilateral kinetic feature asymmetry, we tested 4 different thresholds to select the enhanced pixels (voxels) from DCE-MRI images and compute the kinetic features. Using the optimal threshold, the ANN had a classification performance measured by the area under the ROC curve of AUC=0.79±0.04. The positive and negative predictive values were 0.75 and 0.67, respectively. The study suggested that the bilateral asymmetry of kinetic features or contrast enhancement of breast background tissue could provide valuable supplementary information to distinguish between the malignant and benign cases, which can be fused into existing computer-aided detection schemes to improve classification performance.

Paper Details

Date Published: 18 March 2013
PDF: 9 pages
Proc. SPIE 8670, Medical Imaging 2013: Computer-Aided Diagnosis, 86701F (18 March 2013); doi: 10.1117/12.2007671
Show Author Affiliations
Qian Yang, Hangzhou Dianzi Univ. (China)
Lihua Li, Hangzhou Dianzi Univ. (China)
Juan Zhang, Zhejiang Cancer Hospital (China)
Chengjie Zhang, Hangzhou Dianzi Univ. (China)
Bin Zheng, Hangzhou Dianzi Univ. (China)
Univ. of Pittsburgh (United States)

Published in SPIE Proceedings Vol. 8670:
Medical Imaging 2013: Computer-Aided Diagnosis
Carol L. Novak; Stephen Aylward, Editor(s)

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